Systems and methods for data-driven anomaly detection

Abstract

The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.